Search Results for author:
Found 36 papers, 1 papers with code
Context or No Context? A preliminary exploration of human-in-the-loop approach for Incremental Temporal Summarization in meetings
By compressing the previous contexts by ~70%, we achieve better ROUGE scores over our baseline models.
CueBot: Cue-Controlled Response Generation for Assistive Interaction Usages
Conversational assistants are ubiquitous among the general population, however, these systems have not had an impact on people with disabilities, or speech and language disorders, for whom basic day-to-day communication and social interaction is a huge struggle.
Incremental temporal summarization in multi-party meetings
In this work, we develop a dataset for incremental temporal summarization in a multiparty dialogue.
Cue-bot: A Conversational Agent for Assistive Technology
Intelligent conversational assistants have become an integral part of our lives for performing simple tasks.
AILuminate: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons
This work represents a crucial step toward establishing global standards for AI risk and reliability evaluation while acknowledging the need for continued development in areas such as multiturn interactions, multimodal understanding, coverage of additional languages, and emerging hazard categories.
Safeguard Fine-Tuned LLMs Through Pre- and Post-Tuning Model Merging
In this paper, we address the question: How can we improve downstream task performance while preserving safety in LLMs without relying on additional safety data?
QA-TOOLBOX: Conversational Question-Answering for process task guidance in manufacturing
In this work we explore utilizing LLMs for data augmentation for manufacturing task guidance system.
Decoding Biases: Automated Methods and LLM Judges for Gender Bias Detection in Language Models
However, even with supervised training and human alignment, these LLMs are susceptible to adversarial attacks where malicious users can prompt the model to generate undesirable text.
Introducing v0.5 of the AI Safety Benchmark from MLCommons
We created a new taxonomy of 13 hazard categories, of which 7 have tests in the v0. 5 benchmark.
Zero-shot Conversational Summarization Evaluations with small Large Language Models
Large Language Models (LLMs) exhibit powerful summarization abilities.
Learning from Red Teaming: Gender Bias Provocation and Mitigation in Large Language Models
Recently, researchers have made considerable improvements in dialogue systems with the progress of large language models (LLMs) such as ChatGPT and GPT-4.
Inspecting Spoken Language Understanding from Kids for Basic Math Learning at Home
Enriching the quality of early childhood education with interactive math learning at home systems, empowered by recent advances in conversational AI technologies, is slowly becoming a reality.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Sample Efficient Multimodal Semantic Augmentation for Incremental Summarization
In this work, we develop a prompting approach for incremental summarization of task videos.
Position Matters! Empirical Study of Order Effect in Knowledge-grounded Dialogue
With the power of large pretrained language models, various research works have integrated knowledge into dialogue systems.
Systematic Analysis for Pretrained Language Model Priming for Parameter-Efficient Fine-tuning
To tackle these issues, we propose a general PE priming framework to enhance and explore the few-shot adaptation and generalization ability of PE methods.
End-to-End Evaluation of a Spoken Dialogue System for Learning Basic Mathematics
The advances in language-based Artificial Intelligence (AI) technologies applied to build educational applications can present AI for social-good opportunities with a broader positive impact.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Human in the loop approaches in multi-modal conversational task guidance system development
Development of task guidance systems for aiding humans in a situated task remains a challenging problem.
Learning to Generate Prompts for Dialogue Generation through Reinforcement Learning
Much literature has shown that prompt-based learning is an efficient method to make use of the large pre-trained language model.
NLU for Game-based Learning in Real: Initial Evaluations
Intelligent systems designed for play-based interactions should be contextually aware of the users and their surroundings.
Data Augmentation with Paraphrase Generation and Entity Extraction for Multimodal Dialogue System
Contextually aware intelligent agents are often required to understand the users and their surroundings in real-time.
Seamlessly Integrating Factual Information and Social Content with Persuasive Dialogue
Complex conversation settings such as persuasion involve communicating changes in attitude or behavior, so users' perspectives need to be addressed, even when not directly related to the topic.
Controllable Response Generation for Assistive Use-cases
We build models that can suggest relevant cues in the dialog response context which is used to control response generation and can speed up communication.
Semi-supervised Interactive Intent Labeling
Building the Natural Language Understanding (NLU) modules of task-oriented Spoken Dialogue Systems (SDS) involves a definition of intents and entities, collection of task-relevant data, annotating the data with intents and entities, and then repeating the same process over and over again for adding any functionality/enhancement to the SDS.
Put Chatbot into Its Interlocutor's Shoes: New Framework to Learn Chatbot Responding with Intention
Our framework includes a guiding chatbot and an interlocutor model that plays the role of humans.
Refine and Imitate: Reducing Repetition and Inconsistency in Dialogue Generation via Reinforcement Learning and Human Demonstration
Despite the recent success of large-scale language models on various downstream NLP tasks, the repetition and inconsistency problems still persist in dialogue response generation.
Refine and Imitate: Reducing Repetition and Inconsistency in Persuasion Dialogues via Reinforcement Learning and Human Demonstration
Persuasion dialogue systems reflect the machine's ability to make strategic moves beyond verbal communication, and therefore differentiate themselves from task-oriented or open-domain dialogue systems and have their own unique values.
Audio-Visual Understanding of Passenger Intents for In-Cabin Conversational Agents
To this end, understanding passenger intents from spoken interactions and vehicle vision systems is a crucial component for developing contextual and visually grounded conversational agents for AV.
Low Rank Fusion based Transformers for Multimodal Sequences
In this work, we experiment with modeling modality-specific sensory signals to attend to our latent multimodal emotional intentions and vice versa expressed via low-rank multimodal fusion and multimodal transformers.
Leveraging Topics and Audio Features with Multimodal Attention for Audio Visual Scene-Aware Dialog
With the recent advancements in Artificial Intelligence (AI), Intelligent Virtual Assistants (IVA) such as Alexa, Google Home, etc., have become a ubiquitous part of many homes.
Exploring Context, Attention and Audio Features for Audio Visual Scene-Aware Dialog
Recent progress in visual grounding techniques and Audio Understanding are enabling machines to understand shared semantic concepts and listen to the various sensory events in the environment.
Modeling Intent, Dialog Policies and Response Adaptation for Goal-Oriented Interactions
Building a machine learning driven spoken dialog system for goal-oriented interactions involves careful design of intents and data collection along with development of intent recognition models and dialog policy learning algorithms.
Towards Multimodal Understanding of Passenger-Vehicle Interactions in Autonomous Vehicles: Intent/Slot Recognition Utilizing Audio-Visual Data
Understanding passenger intents from spoken interactions and car's vision (both inside and outside the vehicle) are important building blocks towards developing contextual dialog systems for natural interactions in autonomous vehicles (AV).
Natural Language Interactions in Autonomous Vehicles: Intent Detection and Slot Filling from Passenger Utterances
Understanding passenger intents and extracting relevant slots are important building blocks towards developing contextual dialogue systems for natural interactions in autonomous vehicles (AV).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Context, Attention and Audio Feature Explorations for Audio Visual Scene-Aware Dialog
With the recent advancements in AI, Intelligent Virtual Assistants (IVA) have become a ubiquitous part of every home.
Multimodal Relational Tensor Network for Sentiment and Emotion Classification
Understanding Affect from video segments has brought researchers from the language, audio and video domains together.
Technology Solutions to Combat Online Harassment
This work is part of a new initiative to use machine learning to identify online harassment in social media and comment streams.
